Text summarization and simplification are among the most widely used applications of AI. However, such models are often prone to hallucination, which can result from training models on unaligned data. One efficient approach to address this issue is Loss Truncation (Kang and Hashimoto, 2020), an approach to modify the standard log loss to adaptively remove noisy examples during training. However, we find that LT alone yields a considerable number of hallucinated entities on various datasets. We study the behavior of the underlying losses between factual and non-factual examples, to understand and refine the performance of LT. We demonstrate that LT's performance is limited when the underlying assumption that noisy targets have higher NLL loss is not satisfied, and find that word-level NLL among entities provides better signal for distinguishing factuality. We then leverage this to propose a fine-grained NLL loss and fine-grained data cleaning strategies, and observe improvements in hallucination reduction across some datasets. Our work is available at https://github.com/yale-nlp/Simplification-Projects.

On the Benefits of Fine-Grained Loss Truncation: A Case Study on Factuality in Summarization

Text simplification has emerged as an increasingly useful application of AI for bridging the communication gap in specialized fields such as medicine, where the lexicon is often dominated by technical jargon and complex constructs. Despite notable progress, methods in medical simplification sometimes result in the generated text having lower quality and diversity. In this work, we explore ways to further improve the readability of text simplification in the medical domain. We propose (1) a new unlikelihood loss that encourages generation of simpler terms and (2) a reranked beam search decoding method that optimizes for simplicity, which achieve better performance on readability metrics on three datasets. This study's findings offer promising avenues for improving text simplification in the medical field.

Medical Text Simplification: Optimizing for Readability with Unlikelihood Training and Reranked Beam Search Decoding

This presentation introduces LoFT model, which applies logical forms as fact checkers and content planners to improve faithfulness and diversity of logical table-to-text generation simultaneously.

LoFT: Enhancing Faithfulness and Diversity for Table-to-Text Generation via Logic Form Control

Unfaithful text generation is a common problem for text generation systems. In the case of Data-to-Text (D2T) systems, the factuality of the generated text is particularly crucial for any real-world applications. We introduce R2D2, a training framework that addresses unfaithful Data-to-Text generation by training a system both as a generator and a faithfulness discriminator with additional replacement detection and unlikelihood learning tasks. To facilitate such training, we propose two methods for sampling unfaithful sentences. We argue that the poor entity retrieval capability of D2T systems is one of the primary sources of unfaithfulness, so in addition to the existing metrics, we further propose named entity based metrics to evaluate the fidelity of D2T generations. Our experimental results show that R2D2 systems could effectively mitigate the unfaithful text generation, and they achieve new state-of-theart results on FeTaQA, LogicNLG, and ToTTo, all with significant improvements.

R2D2: Robust Data-to-Text with Replacement Detection

With 84.75 million Filipinos online, the ability for models to process online text is crucial for developing Filipino NLP applications. To this end, spelling correction is a crucial preprocessing step for downstream processing. However, the lack of data prevents the use of language models for this task. In this paper, we propose an N-Gram + Damerau-Levenshtein distance model with automatic rule extraction. We train the model on 300 samples, and show that despite limited training data, it achieves good performance and outperforms other deep learning approaches in terms of accuracy and edit distance. Moreover, the model (1) requires little compute power, (2) trains in little time, thus allowing for retraining, and (3) is easily interpretable, allowing for direct troubleshooting, highlighting the success of traditional approaches over more complex deep learning models in settings where data is unavailable.

Look Ma, Only 400 Samples! Revisiting the Effectiveness of Automatic N-Gram Rule Generation for Spelling Normalization in Filipino

Summarization

poster

Welcome to the 18th Conference of the European Chapter of the Association for Computational Linguistics (EACL). Continuing its mission of expanding and involving the science community of all European countries, EACL has selected the Malta community for the 18th EACL. Considering the importance of physical interaction among researchers, the conference will be held at the Hotel Radisson Blu, St. Julians, in Malta, from 17 to 22 of March, 2024. The conference will also feature the possibility to participate virtually. As the flagship European conference in the field of computational linguistics, EACL welcomes European and international researchers covering a broad spectrum of research areas that are concerned with computational approaches to natural language. 
#### [Conference Handbook](https://drive.google.com/file/d/13Wn38q0yev6U3RTgnf4Tn5T9GuAuZ-xW/view?usp=sharing) *downloadable*

To access the EACL 2024 event page you need to register [**here**](https://acl.swoogo.com/EACL2024). 

Please register!

EACL 2024

As the flagship European conference in the field of computational linguistics, EACL welcomes European and international researchers covering a broad spectrum of research areas that are concerned with computational approaches to natural language.

**Are you attending this poster session virtually?** 
 Virtual posters are presented in Gather Town. Connect by clicking on the link that you can find in the 'Abstracts' tab of the posters.
 
**Are you attending this poster session in person?** 
Virtual posters are presented in Gather Town. Connect by clicking on the link that you can find in the 'Abstracts' tab of the posters.

PS3 (GatherTown) Posters, Demo, Industry, Findings

**Welcome to EMNLP 2023!**

On behalf of the EMNLP 2023 Organizing Committee, I extend a warm and heartfelt welcome to all of you to the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP). It is with immense pleasure and excitement that we gather here in Singapore, a vibrant hub of innovation and technological advancement.

The conference program is packed with insightful presentations, thought-provoking workshops, and engaging networking opportunities. In addition to the technical sessions, we have also planned several social events that will provide you with the opportunity to connect with your colleagues.

The conference is held in person at the Resorts World Convention Centre in Singapore, and available on-line with the help of Underline.

For those in Singapore, I hope that you will find time to explore the exciting Sentosa Island, Gardens by the Bay, Singapore Botanic Gardens and the many other attractions unique to Singapore.

Of course, we are grateful to our sponsors and partners for their generous support of EMNLP 2023, whose contributions make it possible for us to host this world-class event.

Yuji Matsumoto (RIKEN AIP) 
EMNLP 2023 General Chair

To access the **EMNLP 2023** event page on Underline, you need to register for the Conference. 
Please follow **[this link](https://2023.emnlp.org/registration/)** for more details.

EMNLP 2023

EMNLP 2023 took place in Singapore from Dec 6th to Dec 10th, 2023.

**Workshop Organizers:** Yue Dong, Wen Xiao, Lu Wang, Fei Liu and Giuseppe Carenini
 *Coffee Break 10:30 am* 
*Lunch Break 12:30 pm* 
*Coffee Break 3:30 pm* [**Workshop Website**](https://newsumm.github.io/2023/) The Fourth Workshop on “New Frontiers in Summarization” aims to promote the cross-fertilization of ideas in automatic summarization and related fields. This includes discussion on novel paradigms, shared tasks of interest, applied research and applications, and possible future research directions. In addition to building a cohesive research community, the workshop will accelerate knowledge diffusion by developing new tools, datasets, and resources that are in line with the summarization needs of academia, industry, and government. [**Invited Speakers**](https://newsumm.github.io/2023/#Confirmed%20Spearkers) [**Schedule**](https://newsumm.github.io/2023/#Schedule)
 [**Go to Gather**](https://app.gather.town/app/ROH1myZvp9ba7a3C/EMNLP2023?spawnToken=fi01HQsTSTSjo8Ngvolh)

W06: The 4th New Frontiers in Summarization Workshop (NewSumm)

workshop paper

Virtual Poster Session 4

### Welcome to the 17th Conference of the European Chapter of the Association for Computational Linguistics (EACL) 
Continuing its mission of expanding and involving the science community of all European countries, EACL had selected the Ukrainian community for the 16th EACL, which took place online due to the COVID pandemic. Unfortunately, the ongoing war made the organisation in Kyiv impossible. Considering the importance of physical interaction among researchers, especially after the restrictions imposed by the COVID pandemic, in addition to an online mode, the conference will be held in Dubrovnik, Croatia, from 2 to 6 of May, 2023. The original aim of strengthening the connection with the Ukrainian community will not change as our program will feature a dedicated session and a workshop to highlight work on Ukrainian language technologies. As the flagship European conference in the field of computational linguistics, EACL welcomes European and international researchers covering a broad spectrum of research areas that are concerned with computational approaches to natural language.

You need to register for the conference in order to access this site. Please visit https://2023.eacl.org/registration for more information.

EACL 2023

**Topics:** NLP Applications 
NLP Applications (CL) 
Natural Language Generation Demo: Natural Language Generation

Virtual Poster Session 10

## Welcome to EMNLP 2022!
I am delighted to welcome you to EMNLP 2022! I believe this conference has been complicated beyond any precedent. Over the past year, it’s been thrilling to see the organization team approach each new puzzle with creativity and enthusiasm. We hope that those participating in Abu Dhabi as well as those joining remotely will leave the conference feeling newly inspired by the program and newly connected to our ever-growing community. Following EMNLP 2021 and major NLP conferences since, EMNLP 2022 is “hybrid,” serving both virtual and in-person participants.

Our key innovations for EMNLP 2022 include:

* EMNLP 2022 is “hybrid” in a second sense, as well: we allowed both direct and rolling review paper submissions, building on the pilot experiment of EMNLP 2021, which considered a small number of ARR submissions. 
* Familiar from NAACL but new to EMNLP, we’ve added an industry track.
* During the conference, “portals” will link virtual poster sessions to in-person conference participants during poster sessions each day.
* The first ACL-family conference in the United Arab Emirates.

 *Message from Noah A. Smith, University of Washington and Allen Institute for AI, Seattle, Washington, USA* 
***EMNLP 2022 General Chair***
 
[![](https://assets.underline.io/uploads/markdown_image/1/image/9eec7d4a287ee18c278b08229290aa83.png)](https://drive.google.com/file/d/1OlPv6QBeo62VVTughj2jkiLeyHd1WnUt/view)
 
[![](https://assets.underline.io/uploads/markdown_image/1/image/a3db7a768409f05192210d98601edb25.png)](https://emnlp2022.rocket.chat/)

To access this site you need to register. Please register [here](https://2022.emnlp.org/registration/).

Register here

EMNLP 2022

Welcome!
EMNLP 2022 will take place in Abu Dhabi from December 7th to December 11th, 2022. And it will be held in hybrid mode, both online and offline.

[![](https://assets.underline.io/uploads/markdown_image/1/image/26c36e70b5f083b72ae632cbdcc2b5b1.png)](https://us06web.zoom.us/rec/play/A2VNLbC1xoObGgGAXdJADePjdbrI4GfpM-olGlTMO5xp0BM1iN8lgRdm-37i8MQXEvDQHfEvE0hBkjRf.pIMa7Ex7beVmMK-E?autoplay=true&startTime=1670387560000) 

[![](https://assets.underline.io/uploads/markdown_image/1/image/7e41b6370eefd82b0bf5eb67e35a574b.png)](https://us06web.zoom.us/rec/play/CIr3QA3uTAXC4k83Z3LrY1PShqTc_tZI0yVgmzY4ofVG4e2lfSZAOLDzlKcs3qbDceTe8xcfriX6f91P.AdSAW4gIQjDk-2zh?autoplay=true&startTime=1670428311000) 

**Chairs:** Gyuwan KIm, Roy Schwartz, Iryna Gurevych, Roy Schwartz, Andreas Ruckle, Zornitsa Kozareva, Sujith Ravi, Sasha Luccioni and Angela Fan **Organizers:** Nafise Sadat Moosavi, Iryna Gurevych, Angela Fan, Yufang Hou, Zornitsa Kozareva, Sujith Ravi, Sasha Luccioni, Gyuwan Kim, Roy Schwartz, and Andreas Rücklé 
**Invited speakers**: [Hinrich Schutze](https://underline.io/speakers/40293-hinrich-schutze), [Percy Liang](https://underline.io/speakers/99010-percy-liang), [Kurt Keutzer](https://underline.io/speakers/214081-kurt-keutzer) and [Song Han](https://underline.io/speakers/61119-song-han) 
 Please visit our [**website**](https://sites.google.com/view/sustainlp2022/home) for more information and detailed schedule.

**RocketChat channel for this workshop is available here:** https://emnlp2022.rocket.chat/channel/workshop-11-sustainlp

Lorenzo Jaime Flores

6

2

SHORT BIO

Presentations

On the Benefits of Fine-Grained Loss Truncation: A Case Study on Factuality in Summarization

Medical Text Simplification: Optimizing for Readability with Unlikelihood Training and Reranked Beam Search Decoding

Medical Text Simplification: Optimizing for Readability with Unlikelihood Training and Reranked Beam Search Decoding

LoFT: Enhancing Faithfulness and Diversity for Table-to-Text Generation via Logic Form Control

R2D2: Robust Data-to-Text with Replacement Detection

Look Ma, Only 400 Samples! Revisiting the Effectiveness of Automatic N-Gram Rule Generation for Spelling Normalization in Filipino

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